The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

Chinese Academy of Meteorological sciences, 46 Zhongguancun Nandajie, 100081, <strong>Beijing</strong>,<strong>China</strong>
caohx@sina.com
Yang Zheng
National Environmental Protection Administration, Chegezhuangdajie, xxxxxx, <strong>Beijing</strong>
Hai-yan Yu
<strong>Beijing</strong> Bureau of Metrology, 44 Zizhuyuanlu, 100089, <strong>Beijing</strong>, <strong>China</strong>
<strong>The</strong> presence of natural climate variability means that the detection and attribution of
anthropogenic climate change is a statistical ‘signal-in-noise’ problem. Several statistical
methods for detection and attribution of climate change have been developed. Here ‘Granger
causality’ tests are applied to this issue.
‘Granger causality’ tests -- or more correctly perhaps, Granger non-causality tests -- are
statistical tests of ‘causality’ in the sense of determining whether lagged observations of
another variable have incremental forecasting power when added to a univariate
autoregressive representation of a variable. Granger causality is a technique for determining
whether one time series is useful in forecasting another. Ordinarily, regressions reflect ‘mere’
correlations, but Clive Granger, who won a Nobel Prize in Economics, argued that there is an
interpretation of a set of tests as revealing something about causality.
A time series X is said to Granger-cause Y if it can be shown, usually through a series of
F-tests on lagged values of X (and with lagged values of Y also known), that those X values
provide statistically significant information on future values of Y.
<strong>The</strong> test works by first doing a regression of ΔY on lagged values of ΔY. Once the
appropriate lag interval for Y is proved significant (t-stat or p-value), subsequent regressions
for lagged levels of ΔX are performed and added to the regression, provided that they are
significant in and of themselves,and add explanatory power to the model. This can be
repeated for multiple ΔX 's (with each ΔX being tested independently of other ΔX 's, but in
conjunction with the proved lag level of ΔY). More than 1 lag level of a variable can be included
in the final regression model, provided it is statistically significant and provides explanatory
power.
Several climatic variables, such as CO2 concentration in the atmosphere, solar black
point, Atlantic oscillation, East-Asian monsoon etc. are used for testing temperatures in <strong>China</strong>
and in <strong>Beijing</strong> by means of Granger causality test. <strong>The</strong> plausible connections between these
variables and temperatures in <strong>China</strong> and in <strong>Beijing</strong> are revealed. We focus on an impact of the
CO2 concentration on temperatures in <strong>China</strong> and in <strong>Beijing</strong>; the cause of delay of temperature
change in <strong>China</strong> to global warming is explained. Thus it is proved that the Granger causality
test is able to use to detection and attribution of climate change. Besides, Granger causality
test can reveal relationships among different meteorological elements, so it is capable for
screening the predictors for a predictant.
42